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Multimodal MR imaging signatures to identify brain diffuse midline gliomas with H3 K27M mutation

BACKGROUND: Conventional MR imaging has limited value in identifying H3 K27M mutations. We aimed to investigate the capacity of quantitative MR imaging variables in identifying the H3 K27M mutation status of diffuse midline glioma. MATERIALS AND METHODS: Twenty‐three patients with H3 K27M mutation a...

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Autores principales: Su, Xiaorui, Liu, Yanhui, Wang, Haoyu, Chen, Ni, Sun, Huaiqiang, Yang, Xibiao, Wang, Weina, Zhang, Simin, Wan, Xinyue, Tan, Qiaoyue, Yue, Qiang, Gong, Qiyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8855915/
https://www.ncbi.nlm.nih.gov/pubmed/34953060
http://dx.doi.org/10.1002/cam4.4500
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author Su, Xiaorui
Liu, Yanhui
Wang, Haoyu
Chen, Ni
Sun, Huaiqiang
Yang, Xibiao
Wang, Weina
Zhang, Simin
Wan, Xinyue
Tan, Qiaoyue
Yue, Qiang
Gong, Qiyong
author_facet Su, Xiaorui
Liu, Yanhui
Wang, Haoyu
Chen, Ni
Sun, Huaiqiang
Yang, Xibiao
Wang, Weina
Zhang, Simin
Wan, Xinyue
Tan, Qiaoyue
Yue, Qiang
Gong, Qiyong
author_sort Su, Xiaorui
collection PubMed
description BACKGROUND: Conventional MR imaging has limited value in identifying H3 K27M mutations. We aimed to investigate the capacity of quantitative MR imaging variables in identifying the H3 K27M mutation status of diffuse midline glioma. MATERIALS AND METHODS: Twenty‐three patients with H3 K27M mutation and thirty‐two wild‐type patients were recruited in this retrospective study, all of whom underwent multimodal MR imaging. Clinical data and quantitative MR imaging variables were explored by subgroup analysis stratified by age (juveniles and adults). Then, a logistic model for all patients was constructed to identify potential variables for predicting K27M mutation status. Besides, a retrospective validation set including 13 patients was recruited. The C‐index and F1 score were used to evaluate the performance of the prediction model. RESULTS: It turned out that patients with H3 K27M mutation were younger in the adult subgroup. In the mutation group, some relative apparent diffusion coefficient (rADC) histogram parameters and myo‐inositol/creatine plus phosphocreatine (Ins/tCr) ratio were lower than in the wild‐type group of both juveniles and adults (p < 0.05). After nested cross‐validation and LASSO algorithm, the age, Ins/tCr, and rADC_15th were selected as potential predictors for H3 K27M mutation in the model. The nomogram model showed good diagnostic power with a validated C‐index of 0.884. In addition, the area under the curve (AUC) was 0.898 (0.976 in validation set) and the F1 score was 0.732. CONCLUSIONS: In conclusion, age, rADC_15th, and Ins/tCr values were helpful in identifying H3 K27M mutations in midline gliomas.
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spelling pubmed-88559152022-02-25 Multimodal MR imaging signatures to identify brain diffuse midline gliomas with H3 K27M mutation Su, Xiaorui Liu, Yanhui Wang, Haoyu Chen, Ni Sun, Huaiqiang Yang, Xibiao Wang, Weina Zhang, Simin Wan, Xinyue Tan, Qiaoyue Yue, Qiang Gong, Qiyong Cancer Med Clinical Cancer Research BACKGROUND: Conventional MR imaging has limited value in identifying H3 K27M mutations. We aimed to investigate the capacity of quantitative MR imaging variables in identifying the H3 K27M mutation status of diffuse midline glioma. MATERIALS AND METHODS: Twenty‐three patients with H3 K27M mutation and thirty‐two wild‐type patients were recruited in this retrospective study, all of whom underwent multimodal MR imaging. Clinical data and quantitative MR imaging variables were explored by subgroup analysis stratified by age (juveniles and adults). Then, a logistic model for all patients was constructed to identify potential variables for predicting K27M mutation status. Besides, a retrospective validation set including 13 patients was recruited. The C‐index and F1 score were used to evaluate the performance of the prediction model. RESULTS: It turned out that patients with H3 K27M mutation were younger in the adult subgroup. In the mutation group, some relative apparent diffusion coefficient (rADC) histogram parameters and myo‐inositol/creatine plus phosphocreatine (Ins/tCr) ratio were lower than in the wild‐type group of both juveniles and adults (p < 0.05). After nested cross‐validation and LASSO algorithm, the age, Ins/tCr, and rADC_15th were selected as potential predictors for H3 K27M mutation in the model. The nomogram model showed good diagnostic power with a validated C‐index of 0.884. In addition, the area under the curve (AUC) was 0.898 (0.976 in validation set) and the F1 score was 0.732. CONCLUSIONS: In conclusion, age, rADC_15th, and Ins/tCr values were helpful in identifying H3 K27M mutations in midline gliomas. John Wiley and Sons Inc. 2021-12-24 /pmc/articles/PMC8855915/ /pubmed/34953060 http://dx.doi.org/10.1002/cam4.4500 Text en © 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Clinical Cancer Research
Su, Xiaorui
Liu, Yanhui
Wang, Haoyu
Chen, Ni
Sun, Huaiqiang
Yang, Xibiao
Wang, Weina
Zhang, Simin
Wan, Xinyue
Tan, Qiaoyue
Yue, Qiang
Gong, Qiyong
Multimodal MR imaging signatures to identify brain diffuse midline gliomas with H3 K27M mutation
title Multimodal MR imaging signatures to identify brain diffuse midline gliomas with H3 K27M mutation
title_full Multimodal MR imaging signatures to identify brain diffuse midline gliomas with H3 K27M mutation
title_fullStr Multimodal MR imaging signatures to identify brain diffuse midline gliomas with H3 K27M mutation
title_full_unstemmed Multimodal MR imaging signatures to identify brain diffuse midline gliomas with H3 K27M mutation
title_short Multimodal MR imaging signatures to identify brain diffuse midline gliomas with H3 K27M mutation
title_sort multimodal mr imaging signatures to identify brain diffuse midline gliomas with h3 k27m mutation
topic Clinical Cancer Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8855915/
https://www.ncbi.nlm.nih.gov/pubmed/34953060
http://dx.doi.org/10.1002/cam4.4500
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